e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research
and education in agriculture in order to promote Open Science in this field and as such contribute to
addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring
together the relevant scientific communities and stakeholders and engage them in the process of coelaboration
of an ambitious, practical roadmap that provides the basis for the design and
implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

Bacterial, fungal and archaeal microbiota was analysed in 143 chicken faecal samples from a single poultry farm. After DHPLC (denaturing high performance liquid chromatography) 15 bacterial groups, 10 fungal groups and a single archaeal species were differentiated. Samples were grouped into two clusters with significantly different frequencies of C. difficile positive and negative samples in each cluster. Acidaminococcus intestini, described here for the first time as a part of poultry faecal microbiota, was significantly more likely present in C. difficile negative samples, while presence/absence of some other microorganisms (Enterococcus cecorum, Lactobacillus galinarum, Moniliella sp. and Trichosporon asahii) was close to significance. Two other groups not reported previously for poultry, Coprobacillus sp. and Turicibacter sp. did not differ significantly between C. difficile positive and negative samples. Differences in microbiota diversity depend on animal age, but not on the presence of C. difficile. With machine learning (WEKA J48) we have defined specific combinations of microbial groups predictive for C. difficile colonisation. Microbial groups associated with C. difficile colonisation in poultry are different than those reported for humans and include bacteria as well as fungi. Also with this approach A. intestini was found to be most strongly related to C. difficile negative samples. (c) 2013 Elsevier B.V. All rights reserved.

e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.